This report communicates the processes that were followed to assess the quality of the 2019-20 employee census data. For the 2019-20 reporting period:
- The cut-off date for inclusion in the dataset was on 03 September 2020
- The dataset and gender equality indicator (GEI) scorecard were released on 26 November 2020
- 4,943 reporting organisations* were included in the dataset. This covers 11,179 businesses and 4,393,656 employees in the non-public sector.
The WGEA dataset covered 40.3% of the estimated overall Australian workforce. This is slightly down by 1.1 percentage points compared to the previous reporting period. Figure 1 shows that coverage declined for most industries, including retail trade and education & training. Coverage improved for manufacturing and construction and was stable for health care & social assistance.
Figure 1: The 2019-20 WGEA dataset - coverage of all Australian employees
Note: WGEA reporting guidelines require organisations involved in labour hire functions to capture their contracted staff within their WGEA report – this can lead to inconsistency in the capture of these employees between the WGEA and ABS.
Exemptions (Discretion Not to Name (DNN)) granted
The global COVID-19 pandemic restrictions had an impact on the capability of organisations to submit data to the Agency. For the 2019 – 20 reporting period, the Agency granted reporting exemptions (or discretion not to name (DNN)) to 134 reporting organisations across 17 industries. The potential loss of coverage was up to 3.9% across industries. Table 1 shows that the exemptions had a slight impact on the workforce coverage particularly in the arts & recreation, administrative & support services, and retail trade.
Table 1: Coverage impact of exemptions on the 2019-20 dataset by industry
This table is sorted in descending order of coverage impact (%).
|Industry (ANZSIC Division)||Number of reporting organisations exempted||Number of employees in exempted orgs||Employees in the workforce - (Australian Labour Force Survey) ('000)||Coverage impact (%)|
|Arts and recreation services||12||7,915||203.2||3.9|
|Administrative and support services||12||5,292||332.4||1.6|
|Accommodation and Food Services||27||8,066||827.7||0.9|
|Transport, Postal and Warehousing||8||4,405||534.0||0.8|
|Rental, Hiring and Real Estate Services||2||760||154.7||0.5|
|Healthcare and Social Assistance||10||7,205||1629.4||0.4|
|Professional, Scientific and Technical Services||6||3,633||871.4||0.4|
|Public Administration and Safety||2||599||820.2||0.1|
|Agriculture, Forestry and Fishing||1||97||147.6||0.1|
|Education and Training||3||461||1024.8||0.0|
Figure 2 shows that 93.1% of reporting organisations that were not granted DNNs were able to submit verified data to the Agency by the cut-off date. This is an improvement of 1.9 percentage points to the overall timeliness rate, compared to the previous reporting period. Four industries declined in timeliness, most notably administrative & support services and information media & telecommunications. The majority of industries recorded improved their timeliness, including health care & social assistance and manufacturing. Timeliness rates were still above 80% across all the industries
Figure 2: 2019-20 WGEA dataset – timeliness by industry*
*Based on ANZSIC code provided by the reporting organisation
Table 2: Industry breakdown of 15 reports excluded from the 2019-20 dataset
This table is sorted in descending order of coverage impact (%).
|Industry (ANZSIC Division)||Number of reporting organisations excluded||Number of employees in excluded orgs||Employees in the workforce - (ABS Labour Force Survey)||Coverage impact|
|Healthcare and Social Assistance||1||1884||1629.4||0.1|
|Professional, Scientific and Technical Services||6||424||871.2||0.0|
|Financial and Insurance Services||4||26||430.6||0.0|
|Accommodation and Food Services||1||9||827.7||0.0|
|Rental, Hiring and Real Estate Services||1||8||154.7||0.0|
Data quality checks - Questionnaire data
The most common anomalies that were accepted by the Agency as legitimate and included in the dataset relate to the governing bodies data. Table 3 shows that the most common anomaly relate to the target date for female representation falling in the current reporting period, which only affects around 2% of organisations in the dataset.
Table 3: Top 3 questionnaire anomalies in the 2019-20 dataset
|Anomaly||Number of reporting organisations affected||% of organisations in the dataset (4,943)|
|Target % is less than or equal to percentage of women on governing body||111||2.2|
|Too many boards members for orgs||37||0.8|
|Too many chairs for orgs||31||0.6|
Table 4 shows that the accepted anomalies relating to board members greater than 20 and a governing board having more than two chairs accounted for less than 10% of the total board members and chairs in the dataset. The rate of these anomalies will be continually monitored to ensure that they do not exceed the baseline rates as per below.
Table 4: Board member or chair anomalies - records affected 2019-20
|Anomaly||Records affected||Total records in dataset||% of total records|
|Too many board members for orgs||2,443||35,314||6.9|
|Too many chairs for orgs||214||6,728||3.1|
Data quality - remuneration data
Organisations are able to provide remuneration data in unit level or aggregated format. Unit level data enables a richer analysis of remuneration data.
In the 2019 - 20 reporting year, the unit level dataset accounted for 2,272,197 records (52% of the 4,393,656 employee records).
Known limitations of the benchmark’s remuneration data provided by employers in general are summarised below:
- Approximately 0.6% of employee salaries are below $17,300. Most of these salaries are legitimate as some employees are under 15 years of age or are on a disability scheme payment in this dataset. There are legitimate cases where an employee has no salary (for example, in some religious organisations; and when an employee works on commission only).
- Approximately 1.0% of employers reported the same base salary and total remuneration amounts for some employees (noting that this situation can be legitimate under certain circumstances – for example, employees who are under 18 and work less than 30 hours a week, or employees that earn less than $450 a month).
- It is possible that salaries of some part-time or casual employees have not been annualised and/or converted to full-time equivalent amounts, which could lead to more variance in the salary data.
- Table 5 shows that there is more variance in the salary and remuneration unit level data submitted for Full-time employees compared to Part-time and Casual employees. Median absolute deviation (MAD) is used as a measure of variance as it is robust against outlier records.
Table 5: Median absolute deviation (MAD) of annualised salaries 2019 – 20 unit level dataset
|Employment status||Annualised salary MAD||Annualised remuneration MAD|
WGEA census data - changes over time in reporting organisations
The overall size of the comparison group may have changed from last year. The industries granted the most DNNs (Administrative & support services, Arts & recreation services, and Retail trade) decreased, but the sample sizes for these industries are still within the range comparable with the last seven years.
Table 6: Industry breakdown of reporting organisations in WGEA’s benchmark dataset over time
|Accommodation and food services||248||258||260||233||236||257||259|
|Administrative and support services||227||239||253||253||254||267||255|
|Agriculture, forestry and fishing||42||46||47||47||49||52||58|
|Arts and recreation services||98||98||106||100||102||107||102|
|Education and training||491||520||526||512||514||534||542|
|Electricity, gas, water and waste services||51||53||52||47||46||49||47|
|Financial and insurance services||225||238||232||238||254||254||265|
|Health care and social assistance||539||613||652||652||648||668||698|
|Information media and telecommunications||119||125||134||132||136||150||145|
|Professional, scientific and technical services||433||472||488||513||514||550||576|
|Public administration and safety||19||19||22||17||21||30||29|
|Rental, hiring and real estate services||63||72||80||76||82||83||83|
|Transport, postal and warehousing||181||196||190||186||187||192||196|
Organisations may have changed size due to restructure or downsizing (Table 7).
- Organisations may have modified their ownership structure.
- Organisations may have chosen to report in a different way this year (e.g. as a collective last year and as separate subsidiaries this year).
Table 7: Organisation size breakdown of reporting organisations in WGEA’s benchmark dataset over time
|Organisation size category (number of employees)||2013-14||2014-15||2015-16||2016-17||2017-18||2018-19||2019-20|
WGEA census – changes over time in employee numbers
- Table 8 shows that while the number of employees for industries granted the most DNNs (Administrative & support services, Arts & recreation services, and Retail trade) decreased, the employee level for these industries are still within a range comparable to the last seven years.
Table 8: Industry breakdown of employee records in WGEA’s benchmark dataset over time
|Accommodation and food services||173,653||177,140||190,167||202,871||203,434||226,641||225,540|
|Administrative and support services||196,917||211,735||237,001||276,728||305,937||309,210||289,255|
|Agriculture, forestry and fishing||22,379||25,082||27,480||27,716||21,424||23,599||24,719|
|Arts and recreation services||95,105||93,460||95,579||87,645||89,102||91,770||87,666|
|Education and training||381,484||396,159||413,532||408,027||420,626||441,565||436,930|
|Electricity, gas, water and waste services||45,454||47,646||44,226||42,387||43,279||50,321||49,625|
|Financial and insurance services||267,363||275,319||273,307||272,757||273,038||274,570||282,296|
|Health care and social assistance||515,176||559,088||593,819||627,746||655,949||682,519||729,489|
|Information media and telecommunications||131,697||131,798||131,647||128,702||120,508||122,453||117,344|
|Professional, scientific and technical services||288,272||291,561||289,332||276,852||283,413||301,848||318,942|
|Public administration and safety||27,405||25,247||29,569||22,721||34,475||37,115||37,311|
|Rental, hiring and real estate services||34,337||36,450||40,934||41,775||43,844||47,165||44,932|
|Transport, postal and warehousing||207,845||208,998||199,019||195,557||192,749||201,892||214,331|
Table 9: Organisation size breakdown of employee records in WGEA’s benchmark dataset over time
|Organisation size category (number of employees)||2013-14||2014-15||2015-16||2016-17||2017-18||2018-19||2019-20|
Appendix I - 2020 Data Quality Checks
|Data quality check||Explanation of data quality anomaly provided to reporting organisations||Example of a legitimate reason for an anomaly|
|Total remuneration is lower than expected given the base salary entered and superannuation requirements||
The data entered into the 'Total remuneration' column of your aggregate workplace profile is lower than expected when taking into account the base salary entered and superannuation requirements. You may have calculated additional payments (beyond base salary) incorrectly.
If an employee earns less than $450 per month, superannuation is not legally required to be paid. In this case, base salary and total remuneration may be the same.There may be other circumstances where superannuation is not legally required.
|Base salary||The data entered into the 'Base salary' column in your aggregate workplace profile suggests that those salaries may not have been converted to annualised and full-time equivalent amounts. This has been flagged as a possible error because base salaries cannot fall below $35,000, the minimum adult full-time equivalent base salary, or $13,000, the minimum non-adult full-time equivalent base wage.||There are trainees or apprentices, employees on a disability scheme and/or employees under 21 years of age in this occupation.|
|Total remuneration is high||The data entered into the 'Total remuneration' column of your aggregate workplace profile is considerably higher than other employees' remuneration in this occupational category. You may have calculated additional payments incorrectly, or provided the TOTAL remuneration of all employees in a category instead of the AVERAGE remuneration paid to employees in that category.||Employees in this occupation are paid above market rates and/or receive large bonuses or other discretionary payments.|
|Part-time base salary remuneration is less than full-time remuneration||The data entered into the 'Base salary' column for part-time employees in the aggregate workplace profile suggests that those salaries have not been converted to annualised and full-time equivalent amounts, as they are considerably less than the average full-time base salary for that occupation.||Part-time employees are trainees or apprentices, on a disability scheme and/or under 21 years of age.|
|Part-time total remuneration is less than full-time remuneration||The data entered into the 'Total remuneration' column for part-time employees in the aggregate workplace profile suggests that those salaries have not been converted to annualised and full-time equivalent amounts. The current figures are considerably less than the average full-time remuneration for that occupation.
||Part-time employees are trainees or apprentices, on a disability scheme and/or under 21 years of age.|
|Casual base salary remuneration is less than full-time remuneration||The data entered into the 'Base salary' column for casual employees in the aggregate workplace profile suggests that those salaries have not been converted to annualised and full-time equivalent amounts, as they are considerably less than the average full-time base salary for that occupation.||Casual employees are trainees or apprentices, on a disability scheme and/or under 21 years of age.|
|Casual total remuneration is less than full-time remuneration||The data entered into the 'Total remuneration' column for casual employees in the aggregate workplace profile suggests that those salaries have not been converted to annualised and full-time equivalent amounts. The current figures are considerably less than the average full-time remuneration for that occupation.||Casual employees are trainees or apprentices, on a disability scheme and/or under 21 years of age.|
|Reporting levels from CEO are not consecutive||
Your workplace profile contains gaps in the number of reporting levels to the CEO. The reporting levels should be consecutive, e.g. -1,-2,-3, NOT e.g. -1,-3,-4 where -2 is missing. Please see page 25 of the Reference Guide for more detail.
|Equity partners usually occupy an entire reporting level. For example, they represent the entire '-1' level as they report directly to the CEO/head of business.|
|All manager categories report to someone higher that CEO||All of your managers have been allocated a reporting level to the CEO of “+1”. This reporting level is only to be used for those individuals who are based overseas and who are more senior than the CEO. This usually applies to only a few individuals in global organisations.||
It is a multi-national organisation which employs managers in Australia who: are more senior in the hierarchy than the Australian CEO/head of business, and report to someone overseas.
|Multiple CEOs||Your workplace profile has multiple CEOs. This may be because other categories of managers have been incorrectly classified as CEOs. Managers need to be grouped into the five standardised management categories according to definitions provided in the Reference Guide. Please see page 21 of the Reference Guide for more detail.||The report covers multiple organisations with registered ABNs, and/or the CEO/head of business role is shared.|
|100 percent of workforce is one gender||Your workplace profile shows that 100 percent of your workforce is one gender.||Only females/males were employed at the time of reporting.|
|One or more CEO and no other managers||
Your workplace profile contains no data for manager categories other than CEO. This means your company has one or more CEOs but no managers. If this is an error remember managers need to be grouped into the five standardised management categories according to the definitions provided in the Reference Guide, then their reporting level to the CEO indicated.
|The organisation only has a CEO/head of business and no other managers.|
|More than 20 percent of non-managers are classified in the ‘other’ occupational category||20% or more non-managers have been categorised as being employed in the occupational category of "Other". This appears to be an error as it means a significant portion of your employees do not fit into the standard occupation categories for non-managers.||Employee works in a highly specialised or unique role that genuinely cannot be categorised in any other non-manager category.|
|More than 20% of non-managers are classified as graduates||You have categorised 20 percent or more non-managers as being employed as 'graduates'. This appears to be an error as it means at least one in five non-manager employees are on a formal graduate program.||There was a high intake of employees in the formal graduate program and/or the graduate program spans multiple years.|
|Workforce does not contain any managers||Your workplace profile contains no managers. Managers need to be grouped into the five standardised management categories according to definitions provided in the Reference Guide, then the reporting level to the CEO indicated.||It is a global organisation and does not have any managers employed in Australia.|
|Organisation does not have a CEO/Head of Business||Your workplace profile does not include a CEO. You do not need to include salary data, however you must include the gender and employment status of your CEO.||The organisation has been placed into administration and there is no CEO/head of business.|
|No directors on the governing body/board||Your response to Q2.1 of the reporting questionnaire shows that you have no board chairs or members for at least one of your governing bodies.||There are multiple owners and they are all recorded as chairs|
Too many board members for orgs
|Your response to Q2.1 of the questionnaire shows the number of board members is more than 20 for one or more of your governing bodies.||The organisation is a union, religious or other organisation with a high number of boards.|
|Too many chairs for orgs||Your response to Q2.1 of the questionnaire shows that at least one of your governing bodies has more than two chairs.||The organisation is a union, religious or other organisation with a high number of boards.|
|The target set for the governing body is less than or equal to the representation of women on the board. The target has already been reached||Your response to Q2.1 in the reporting questionnaire shows that the target set for one or more of your governing bodies is less than or equal to the representation of women on your board.||The target date falls in the reporting period or later.|
|Primary Carers Leave - high number of weeks||Your response to Q5.1 of the questionnaire shows the number of weeks offered for primary carer's leave is more than one year (52 weeks). That may be an error as it is an unusually large amount of leave.||Industry standard|
|Secondary Carers Leave - high number of days||Your response to Q6.1 of the questionnaire shows the number of days offered for secondary carer’s leave is more than six months (approximately 180 days). That may be an error as it is an unusually large amount of leave.||Industry standard|
|0 weeks of paid primary carer's leave has been entered, despite the organisation indicating that it does offer primary carer's leave.||Your response to Q5.1 of the reporting questionnaire shows that you offer 0 weeks of primary carer's leave, despite your organisation indicating it does offer paid primary carer's leave.||None|
|0 days of paid secondary carer's leave has been entered, despite the organisation indicating that it does offer secondary carer's leave.||Your response to Q6.1 of the reporting questionnaire shows that you offer 0 days of secondary carer's leave, despite your organisation indicating it does offer paid secondary carer's leave.||None|
|Number of resignations while on parental leave is greater than the number of employees on parental leave||Your responses to Q7 and Q8 of the reporting questionnaire show the number of female manager resignations while on parental leave is considerably larger than the total number of female managers who utilised parental leave.||None|
|Number of promotions is greater than the number of appointments||Your response to Q1.11 in the reporting questionnaire shows the number of promotions is larger than the total number of appointed, however the number of appointments should also include the number of promotions.||None|